In this paper,
we present ELM,
a framework that enforces both
Embedding
and Logit Margins.
In a nutshell, ELM enforces margins
in logit space, and regularize the distribution of embeddings.
This
connects losses for long-tail learning to proposals in the literature
on metric learning [1]}, and contrastive learning [2]}.
Theoretically, we show
how ELM encourages a better approximation to the Bayes solution, by
ensuring that class-conditionals are more Gaussian.
Empirically, ELM is
shown to perform well, and results in tighter embeddings
(cf.  Figure REF ).
In sum, our contributions are:
